Edge Object Detection Model Retraining via Noise Augmentation
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Solution Overview
Problem
Machine learning models for object detection and classification face accuracy issues when deployed in edge devices due to environmental noise conditions differing from those in the training data, leading to decreased performance and increased system latency.
Innovation Solution
A processing device at the edge device selects baseline images based on noise characteristics and augments them to generate training data that matches the current environmental conditions, allowing for continuous retraining of the model to improve accuracy under varying noise conditions without interrupting other high-priority processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the machine learning model is trained using standard training data without environmental matching, then the training process is simple and fast, but the detection accuracy decreases under varying environmental noise conditions
Solution Approach 1:
The patent applies parameter changes by transforming the training data through noise augmentation to match environmental conditions. The system modifies the training samples by adding environmental noise characteristics, thereby changing the data parameters to align with real-world deployment conditions and improve detection accuracy under varying noise levels.
Solution Approach 2:
The system performs preliminary action by pre-processing training data to match environmental conditions before actual detection. The training data is augmented with noise characteristics in advance, so that when the model is deployed, it is already adapted to environmental variations without requiring real-time adjustments during detection.
2Measurement precision
If the model is continuously retrained with augmented training samples, then the detection accuracy under varying conditions improves, but the processing time and system latency increase
Solution Approach 1:
The system implements periodic action by scheduling continuous training operations at intervals rather than continuously. The augmented training samples are generated and applied periodically, allowing the model to be updated at optimal moments without constant retraining, thereby balancing accuracy improvement with acceptable system latency.
3Adaptability or versatility
If augmented training samples are generated and applied continuously, then the model adapts to environmental conditions, but the computational resources and processing overhead increase
Solution Approach 1:
The system applies self-service by automatically generating augmented training samples and applying them to the model without requiring external intervention. The system autonomously identifies environmental conditions, creates matching training data, and retrains the model, reducing the need for manual data preparation and external computational resources.
4Measurement precision
If the training data is augmented to match real-time environmental conditions, then the detection precision improves, but the data processing complexity and storage requirements increase
Solution Approach 1:
The system uses parameter changes by modifying existing training data through noise augmentation rather than creating entirely new datasets. By transforming the parameters of existing samples to match environmental conditions, the system achieves improved classification accuracy without proportionally increasing data storage requirements or processing complexity.
Data Source
AI summary
Apparatuses, systems, and techniques for continuous training of an object detection and/or classification model. A first image including a depiction of an environment based on a first set of conditions is identified. Object data associated with an object detected in the first image is obtained based on one or more outputs of a machine learning model. A determination is made of whether a level of confidence that an object corresponds to an object class satisfies a level of confidence criterion. If so, a first set of conditions corresponding to the environment depicted in the first image. One or more noise characteristics associated with a second image including a depiction of the environment is determined based on a difference between the first set of conditions and a second set of conditions of the second image. The first image is augmented based on the one or more determined noise characteristics to generate a third image. The third image reflects the depiction of the environment based on the second set of conditions and a depiction of the object detected in the first image. Training data is associated with the third image is provided to train the machine learning model.